Abstract
We exploit the connection between boosting and greedy coordinate optimization to produce new accelerated boosting methods. Specifically, we look at increasing block sizes, better selection rules, and momentum-type acceleration. Numerical results show training convergence gains over several data sets. The code is made publicly available.
Xiaomeng Ju
Postdoctoral research fellow in Biostatistics
I am a postdoctoral research fellow in the Division of Biostatistics, at the New York University, Grossman School of Medicine, advised by Professor Matias Salibian-Barrera. She received her BSc in Statistics from Renmin University of China, and MA in Statistics from University of Michigan. Xiaomeng’s research is centred on computational statistics with a special focus on robust statistics and functional data. Her ongoing thesis work develops gradient boosting methods for regression problems with complex data.